In a computer mediated communication network, which is provided by social networking service such as Twitter™, many messages are posted each day, each second. Some of the messages can be forwarded from one user to another user, repeatedly, causing information diffusion starting from an originator over the communication network. Visualizing diffusion flows of such forwarded messages is important in order to understand a major diffusion route through which information flows easily and to identify an influencer who has significant influence in the network.
However, forwarding mechanisms, e.g. a retweet functionality of Twitter™, can provide typically only information about an original user who has posted a message originally and a forwarding user who has forwarded the message of the original user. The forwarding user may or may not read the original user's message directly, and can read the original message via another user's forwarded message. Information about a user who has posted or forwarded the message that the forwarding user actually read can be lost.
There is a need for efficiently and plausibly estimating an information diffusion route on a computer mediated communication network without requiring direct information of diffusion paths between users.